Images of outdoor scenes are usually degraded by atmospheric particles, such as haze, fog and smoke, which fade the color and reduce the contrast of objects in the scene. Poor visibility becomes a major problem for outdoor video surveillance applications. Haze removal is difficult because the amount of visible haze is dependent on depth information. The depth information is unknown given only a single image, making the de-haze problem ill-posed. Even when the depth information can be estimated, noise can be a major problem when restoring hazy images. Standard image enhancement algorithms, such as histogram equalization, linear mapping, and gamma correction, introduce halo artifacts and color distortion. Existing de-hazing approaches rely on sophisticated numerical optimization algorithms, which result in high computational costs and cannot satisfy real-time application requirements, especially for a high resolution video surveillance system.
Existing de-hazing processes for single images follow the same steps: estimate the veil (due to haze) in the image at each pixel location, and then apply a restoration algorithm to the pixel value. Most of these processes use a sophisticated model to estimate the veil, which results in high computation costs. Recent work on single image de-hazing has made significant progress. He et al. (see the List of Incorporated Cited Literature References, Literature Reference No. 2) proposed a method based on a so-called dark channel prior for single image haze removal. Using this method, a coarse estimation of the transmission map is obtained by dark pixels in local windows and then refined by an image soft-matting technique. Furthermore, Fattal (see Literature Reference No. 4) obtained the transmission map through independent component analysis by assuming that transmission and surface shading are locally uncorrelated. In addition, Tan (see Literature Reference No. 3) aimed to maximize the local contrast by developing a cost function in the framework of Markov random fields (MRFs).
A common problem of the above methods is that they are too slow for real-time applications. For example, the method of He et al. (see Literature Reference No. 2) takes approximately 250 seconds for a 3000 by 400 pixels image on a modern computer. Moreover, the method of Fattal (see Literature Reference No. 4) is reported to have bad performance for heavy haze images, and the method of Tan (see Literature Reference No. 3) suffers from a very saturated scene.
Tarel et al. (see Literature Reference No. 1) proposed a median filter-based process to solve the single image de-hazing problem. It is faster than the processes described in Literature Reference Nos. 2, 3, and 4, since its complexity is only a linear function of the number of input image pixels. However, it still takes 0.6 seconds on the same computer processing unit (CPU) for an image of 3000 by 400 pixels, which is still too slow for real-time video surveillance applications. In addition, Tarel's method estimates the global atmospheric light from the pixels with high intensity, which may lead to incorrect global atmospheric light estimation due to white objects in the scene.
Thus, a continuing need exists for a robust and fast image de-hazing process for real-time video processing.